sf_trees <- read_csv(here("data", "sf_trees", "sf_trees.csv"))
## Parsed with column specification:
## cols(
## tree_id = col_double(),
## legal_status = col_character(),
## species = col_character(),
## address = col_character(),
## site_order = col_double(),
## site_info = col_character(),
## caretaker = col_character(),
## date = col_date(format = ""),
## dbh = col_double(),
## plot_size = col_character(),
## latitude = col_double(),
## longitude = col_double()
## )
Refresh some skills for data wrangling & summary statistics using functions in the dplyr package.
Find the top five highest observations of trees by legal_status, do some wrangling, make a graph.
top_5_status <- sf_trees %>%
count(legal_status) %>% #recognizes groups, finds counts, and puts in a nice table (groupby, summarize, n all in one!)
drop_na(legal_status) %>%
rename(tree_count = n) %>%
relocate(tree_count) %>% #in dplyr, moves variables around, in this case we relocated tree_count to the first column position
slice_max(tree_count, n = 5) #allows you to identify the rows with the highest values for a variable that you specify and only keeps the top number that you want
Make a graph of those top 5 observations by legal status.
ggplot(data = top_5_status, aes(x = fct_reorder(legal_status, tree_count), y = tree_count)) +
geom_col() +
labs(x = "Legal Status", y = "Tree Count") +
coord_flip() +
theme_minimal()
Only want to keep observations (rows) for Blackwood Acacia trees.
blackwood_acacia <- sf_trees %>%
filter(str_detect(species, "Blackwood Acacia")) %>%
select(legal_status, date, latitude, longitude)
ggplot(data = blackwood_acacia, aes(x = longitude, y = latitude)) +
geom_point()
## Warning: Removed 27 rows containing missing values (geom_point).
Useful for combining or separating columns.
sf_trees_sep <- sf_trees %>%
separate(species, into = c("spp_scientific", "spp_common"), sep = "::")
Example: tidyr::unite()
sf_trees_unite <- sf_trees %>%
unite("id_status", tree_id:legal_status, sep = "_cool!_")
st_as_sf() to convert latitude and longitute to spacial coordinates.
blackwood_acacia_sp <- blackwood_acacia %>%
drop_na(longitude, latitude) %>%
st_as_sf(coords = c("longitude", "latitude"))
st_crs(blackwood_acacia_sp) = 4326
ggplot(data = blackwood_acacia_sp) +
geom_sf(color = "darkgreen")
Read in SF roads shapefile:
sf_map <- read_sf(here("data", "sf_map", "tl_2017_06075_roads.shp"))
st_transform(sf_map, 4326)
## Simple feature collection with 4087 features and 4 fields
## geometry type: LINESTRING
## dimension: XY
## bbox: xmin: -122.5136 ymin: 37.70813 xmax: -122.3496 ymax: 37.83213
## geographic CRS: WGS 84
## # A tibble: 4,087 x 5
## LINEARID FULLNAME RTTYP MTFCC geometry
## * <chr> <chr> <chr> <chr> <LINESTRING [°]>
## 1 110498938… Hwy 101 S O… M S1400 (-122.4041 37.74842, -122.404 37.7483, -…
## 2 110498937… Hwy 101 N o… M S1400 (-122.4744 37.80691, -122.4746 37.80684,…
## 3 110366022… Ludlow Aly … M S1780 (-122.4596 37.73853, -122.4596 37.73845,…
## 4 110608181… Mission Bay… M S1400 (-122.3946 37.77082, -122.3929 37.77092,…
## 5 110366689… 25th Ave N M S1400 (-122.4858 37.78953, -122.4855 37.78935,…
## 6 110368970… Willard N M S1400 (-122.457 37.77817, -122.457 37.77812, -…
## 7 110368970… 25th Ave N M S1400 (-122.4858 37.78953, -122.4858 37.78952,…
## 8 110498933… Avenue N M S1400 (-122.3643 37.81947, -122.3638 37.82064,…
## 9 110368970… 25th Ave N M S1400 (-122.4854 37.78983, -122.4858 37.78953)
## 10 110367749… Mission Bay… M S1400 (-122.3865 37.77086, -122.3878 37.77076,…
## # … with 4,077 more rows
ggplot(data = sf_map) +
geom_sf()
Now combine blackwood acacia tree observations and SF roads map:
ggplot() +
geom_sf(data = sf_map, size = 0.1, color = "darkgray") +
geom_sf(data = blackwood_acacia_sp, color = "darkgreen", size = 0.5) +
theme_void()
Now an interactive map:
tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(blackwood_acacia_sp) +
tm_dots()